Can fisheries bioenergetics modelling refine spatially explicit assessments of climate change vulnerability?

We used bioenergetics models to project the physiological impacts of climate warming across the geographic range of Guadalupe bass, Micropterus treculii, and compared these projections to changes in habitat suitability from traditional species distribution models. This comparison revealed complementary insights that can inform conservation of freshwater fishes experiencing climate change.


Introduction
Climate change poses a significant threat to global biodiversity in the 21st century. Conservation planners need vulnerability assessments to identify and prioritize which species and regions are most in need of conservation action. Many of these assessments implement correlative species distribution modelling (SDM; sensu Elith and Leathwick, 2009) because the input data-georeferenced occurrence records and geographic information system (GIS)-based climate layers-are profuse and easily accessible (Anderson et al., 2016;Fick and Hijmans, 2017). This approach facilitates the assessment of many species across broad geographic areas. For example, Crossman et al. (2012) used the magnitude of SDM-projected distributional shifts between historical and future climates to characterize vulnerability of 171 southern Australian plant species. In a similar study of 270 Australian Odonate dragonflies and damselflies, Bush et al. (2014) identified taxa that are most vulnerable to climate change and identified priority regions for conservation actions such as regulating water extraction and protecting refugia habitats. Numerous other assessments have been performed for a variety of regions and taxa, including freshwater fishes (Herrera-R et al., 2020;Perkin et al., 2021;Troia et al., 2019). These SDM-based assessments are invaluable to conservation planners because they incorporate many species in the assessment, are spatially contiguous and span broad geographic extents.
A limitation of SDM-based assessments is that they do not explicitly assess physiological impacts of climate change. This can lead to uncertain and inaccurate vulnerability assessments because the realized climatic niche in the present may not reflect the fundamental climatic niche (Kearney and Porter, 2009;Sax et al., 2013). Recent assessments are beginning to estimate the physiological impacts of climate change across broad spatial extents. For example, Comte and Olden (2017) used laboratory-derived upper thermal tolerances of fishes and global temperature projections to assess global patterns of vulnerability among freshwater and marine fishes. This assessment revealed high warming exposure for temperate freshwater faunas versus high physiological sensitivity for temperate marine faunas. Similarly, Dillon et al. (2010) found that metabolic sensitivity of terrestrial ectotherms is higher for tropical taxa compared with temperate and polar taxa, suggesting that physiological impacts of climate warming will be greater in the tropics despite relatively moderate rates of warming. These physiology-based assessments are important because they can reveal causal links between specific climatic conditions and the performance of individuals and populations. For example, Briscoe et al. (2016) showed that timing of rainfall events relative to extreme heat events limit survival of koalas at their contemporary range edges and will likely drive shifts in these range edges under future climate.
Freshwater fishes are diverse, imperilled and in need of vulnerability assessments and conservation action (Strayer and Dudgeon, 2010;Vörösmarty et al., 2010). Bioenergetics modelling (BEM) has been used in fisheries science since the 1970s to inform the management of recreational and commercial fish stocks (Augustine and Kooijman, 2019;Deslauriers et al., 2017). The 'Wisconsin model' (sensu Deslauriers et al., 2017) uses a species-specific energy balance equation to estimate energy gains-via consumption of prey, and energy losses, via metabolism, egestion and excretion-to estimate daily energy budgets of individuals. These energy balance equations are based on intrinsic physiological and behavioural traits of the species and on extrinsic environmental conditions. Prolonged periods of bioenergetic deficits lead to mortality, whereas bioenergetic surpluses are directed towards somatic growth and reproduction (Garvey et al., 2004;Lawrence et al., 2015). BEM provides a physiology-based approach to assess climate change vulnerability of fishes that go beyond assessments based on physiological thermal limits (e.g. Comte and Olden, 2017;Deutsch et al., 2008;Feeley and Silman, 2010). Pease and Paukert (2014) used BEM to estimate prey consumption and growth potentials for four populations of smallmouth bass (Micropterus dolomieu) under historical and future thermal regimes. Nevertheless, there are few such applications of BEM. It is surprising that BEM has not been used for broader-scale climate change vulnerability assessments given (i) the accumulation of species for which BEM are parameterized (i.e. at least 70 species for the Wisconsin model; Deslauriers et al., 2017); (ii) the accessibility of GIS data layers describing environmental conditions in freshwater habitats (Hill et al., 2016); and (iii) conceptual development of BEM for conservation of rare species (Petersen et al., 2008).
Here, we present an assessment of climate change vulnerability for the Guadalupe bass (Micropterus treculii)a fish endemic to the Edwards Plateau region of central TX, USA (Fig. 1A). The small geographic range, only 20 000 to 200 000 km 2 (NatureServe, 2010), and the ongoing threat of hybridization with non-native smallmouth bass (M. dolomieu; Bean et al., 2013) make Guadalupe bass a species of conservation concern (Hubbs et al., 2008). Guadalupe bass are also popular among recreational anglers, meaning the persistence of robust populations is of cultural and economic importance for the region (Thomas, 2015). Still, vulnerability of Guadalupe bass to 21st century climate change remains unknown. To assess climate change vulnerability, we used BEM to project changes in bioenergetic budgets of individuals and correlative SDMs to project changes in reach occupancy under historical and end of 21st century climate scenarios. Because BEM parameters do not yet exist for Guadalupe bass, we compiled parameters of largemouth bass (Micropterus salmoides) and used a novel parameter resampling algorithm to develop and validate an ensemble of BEMs. SDMs were parameterized with open-source occurrence records and landscape and climatic covariates using the Maximum Entropy algorithm (Phillips et al., 2004;Vollering et al., 2019). Both models were projected to 7872 perennial confluence-to-confluence stream reaches within the Guadalupe bass range. By comparing occurrencebased and physiology-based approaches using this broadscale, spatially explicit framework of SDM, we provide novel insight into how climate change will (i) affect multiple levels of the biological hierarchy and (ii) vary across Guadalupe bass geographic range, including where vulnerability (or benefits) will be greatest. Implementing BEM on a daily time step also reveals seasonal variation in bioenergetic budgets, providing insight into another dimension of vulnerability-seasonal changes in physiological performance.

Study system
The Guadalupe bass is native to the Brazos, Colorado, Guadalupe and San Antonio river basins and is introduced to the Nueces river basin of TX, USA (Fig. 1A). Streams of these basins flow generally from northwest to southeast. Most of these basins drain the High Plains in the west, Edwards Plateau in the center and the Coastal Plain in the east. Precipitation declines markedly from 1360 mm per year in the east to 345 mm per year in the west and mean annual air temperature ranges from 13.5 • C in the northwest to 22.1 • C in the south. We implemented SDMs and BEM for all perennial, confluence-to-confluence stream reaches (hereafter 'reaches') within the five basins. We acquired reaches as a polyline shapefile from the National Stream Internet flowline layer  and retained perennial reaches (NHD FCode = 46 006), yielding 7872 reaches.

Species distribution modelling
We used SDM to map the habitat suitability of Guadalupe bass to all 7872 reaches in the study region. We extracted records of Guadalupe bass occurrence (n = 240) between 1950 and 1980 from the IchthyMaps dataset (Frimpong et al., 2016) (Fig. 1A). We acquired GIS-derived predictor variables describing hydrographic, geological and soil characteristics (hereafter 'landscape' variables) for the 7872 reaches from the StreamCat dataset (Hill et al., 2016). We acquired averages of monthly precipitation totals and monthly air temperature minima, means and maxima (hereafter 'climate' variables) for all reaches between 1960 and 1990 using the ClimateNA program (Wang et al., 2016). Although air temperature and precipitation are not direct measures of parallel in-stream conditions (water temperature and flow, respectively), these climatic conditions are effective surrogate covariates for modelling habitat suitabilities of stream-dwelling fishes (McGarvey et al., 2018). We checked variable pairs for correlations (Pearson's r) and retained only uncorrelated variables (|r| ≤ 0.7) known to affect in-stream habitat suitability of stream fishes (Huang and Frimpong, 2015) (Supplementary  Table S1).
We fit SDMs with the Maximum Entropy algorithm using the MIAmaxent library in R (Vollering et al., 2019). This model was used to project habitat suitability to all 7872 reaches under seven different projections, each representing a different combination of climate scenarios (Table 1). For projections to future climates (projections 2-7), we used scenarios of moderate (Representative Concentration Pathway; hereafter 'RCP 4.5') and high (RCP 8.5) greenhouse gas emissions derived from an ensemble of 15 general circulation models (Wang et al., 2016). Because we were interested in exploring effects of temperature and precipitation changes independently of one another-and comparing temperatureonly effects to BEM projections-we performed projections in which both temperature and precipitation change (projections 2 and 5), only temperature changes (3 and 6) or only precipitation changes (4 and 7). We used the log 2 of the probability ratio output (hereafter 'PRO') as habitat suitability and interpreted reaches with values ≥1 as being occupied following Vollering et al. (2019). We defined four historical to future projection states: persist (occupied to occupied), colonize (unoccupied to occupied), extirpate (occupied to unoccupied) and unoccupied (unoccupied to unoccupied). Our definition of 'occupied' is based strictly on habitat suitability of the reach and does not account for dispersal limitation, particularly as it relates to the 'colonize' state.
We evaluated model performance by splitting records into a 50% training set (n = 120), fitting the model and computing the area under the curve (AUC) of the receiver operator characteristic on the remaining records (testing set; n = 120 records). Because the IchthyMaps dataset does not include putative absence records, we selected a random sample (equal to the number of records in the testing set; n = 120) of reaches in which a congener (Micropterus spp.) was present in the IchthyMaps dataset and Guadalupe bass was not present. The presence of an ecologically similar species and not the target species increases the likelihood of selecting true absences compared to selecting random reaches (with or without) IchthyMaps records (Huang and Frimpong, 2015;Mateo et al., 2010). This independent cross-validation procedure was repeated 10 times, each time selecting different subsets of records for training and testing. The mean AUC of these 10 repetitions was computed and used to evaluate model performance. We evaluated variable performance by removing each variable, in turn, and computing the percent reduction in AUC relative to the model with all variables. To evaluate the predictive capability of landscape versus climate variables, we evaluated model performance of models fit with (i) landscape and climate variables, (ii) landscape variables only and (iii) climate variables only.

BEM-model descriptions
We used BEM to map somatic growth potential (hereafter 'growth') of Guadalupe bass to all 7872 reaches in the study region. This is the Wisconsin bioenergetics model, which follows the equation from Hanson et al. (1997): where G is energy gained via growth, C is energy gained via consumption, R is energy lost via respiration, SDA is  energy lost via specific dynamic action (the energetic cost of digestion), E is energy lost via egestion and U is energy lost via excretion. Mass dependence of respiration and consumption are modelled as power functions of the form: where R and C are mass-specific routine metabolism measured in grammes of O 2 consumed per gramme of body mass and maximum consumption as grammes of prey consumed per gramme of body mass, respectively; M is mass; R A and C A are slopes; and R B and C B are y-intercepts. f(T R ) and f(T C ) are temperature dependence of respiration and consumption, represented by Equations 4 and 5, respectively. BEM also require inputs of energy density, the proportion of maximum consumption at which an individual feeds (C P ), and the activity multiplier of respiration (R ACT ) that takes into account the metabolic costs of activity beyond routine metabolic rate (e.g. searching for and pursuing prey). We converted O 2 consumption to joules using the oxycalorific coefficient, 13 560 J/g O 2 (Hanson et al., 1997) and grammes of prey consumed to joules using the median energy density reported for larval fish, the presumed prey of age-1 Guadalupe bass (3698 J/g wet mass; Henderson and Ward, 1978). The temperature dependence equation for respiration follows the exponential form presented by Hanson et al. (1997): where f(T R ) is mass-specific routine respiration, R Q is the slope of the respiration function at low water temperatures and T is temperature. The temperature dependence equation for consumption follows the monotonic form presented by Hanson et al. (1997) after Kitchell et al. (1977): where where f(T C ) is mass-specific consumption, CTM is the temperature above which feeding ceases, CTO is laboratory temperature preferendum and C Q approximates a Q 10 (i.e. the rate at which consumption increases at low temperature). We assumed egestion and excretion to be constant proportions of consumption and specific dynamic action to be a constant proportion of respiration (Hanson et al., 1997).
Because laboratory measurements of the mass-dependence and temperature-dependence of consumption and respiration as well as energy density have not been published for Guadalupe bass (Deslauriers et al., 2017), we borrowed these BEM parameters from largemouth bass (Supplementary Table  S2). Largemouth bass is the most recent common ancestor of Guadalupe bass, having diverged approximately 5.2 million years ago (Near et al., 2003), and occupies a climatic envelope that contains Guadalupe bass (NatureServe, 2010). As such, largemouth bass is the best surrogate species currently available for modelling bioenergetics budgets of Guadalupe bass (Petersen et al., 2008). Mass-dependent respiration and consumption were modelled as power functions (Equations 2 and 3). Temperature-dependent respiration was modelled as an exponential function (Equation 4) and temperaturedependent consumption was modelled as a monotonic function (Equation 5). Because it is unlikely that BEM parameters of Guadalupe bass are precisely equivalent to largemouth bass, we performed a parameter resampling procedure to generate additional parameter sets (hereafter 'synthetic parameter sets'). For each parameter, we sampled from a uniform distribution bounded by a minimum value 5% lower than the largemouth bass parameter value and a maximum value 5% higher than the largemouth bass parameter value. These bounds generated parameter values that are within the range of parameter values observed for other fish species (Deslauriers et al., 2017), and therefore are biologically realistic. We used this procedure to generate 500 synthetic parameter sets.

BEM-validation
We validated BEM using empirical growth of a Guadalupe bass population in the South Llano River. We estimated agespecific growth using back-calculated length-at-age measurements of scales taken from fish spawned between 2005(Groeschel-Taylor et al., 2019. Lengths-at-age were converted to masses-at-age using von Bertalanffy equations of Guadalupe bass (Groeschel-Taylor et al., 2019). We used BEM to project growth beginning on Julian day 1 and ending on Julian day 365. These projections require four inputs: (i) initial fish mass, (ii) proportion of maximum consumption at which an individual feeds (C P ), (iii) the activity multiplier of respiration (R ACT ) and (iv) mean daily water temperature during the projection period. Fish mass was assumed to be 6.4 g for age-1 fish (Groeschel-Taylor et al., 2019), C P to be 0.5 and R ACT to be 1.0. We estimated mean daily water temperatures for all 17 reaches within the South Llano River study area of Groeschel-Taylor et al. (2019) using a two-step approach. First, we acquired monthly mean air temperatures at each reach from the ClimateNA program (Wang et al., 2016) for the years during which growth was measured (2005 through 2011). We converted air temperature to water temperature using sigmodal air-water temperature relationships derived by Punzet et al. (2012) for the warm-temperate Köppen-Geiger climate zone. Second, we fit a sine function to the annual temperature cycle at each reach, separately, using the monthly water temperatures and assuming these monthly values correspond to the middle day of each month (i.e. Julian days 15, 46, 74, 105, 135, 166, 196, 227, 258, 288, 319 and 349). We then used these sine functions to interpolate water temperature to all 365 Julian days at the 17 reaches.
We simulated growth for age-1 individuals using each of the 500 synthetic parameter sets, and identified those that projected end-of-growing-season simulated length within ±2.5% of end-of-year empirical length and retained these BEM for the climate change vulnerability assessment described below. We used simulated length at the end-ofgrowing-season (i.e. last Julian day with a daily bioenergetics surplus) because mass declines during the remaining days of the calendar year whereas length does not. Hence, using simulated end-of-year mass and converting it to length would underestimate actual length gained during the calendar year.

BEM-projecting bioenergetics to reaches
We used only the validated parameter sets to project growth of age-1 Guadalupe bass for all 7872 reaches assuming climate under the historical time period and four future scenarios: (i) RCP 4.5 emissions scenario with baseline consumption (C P = 0.5), (ii) RCP 8.5 with baseline consumption (C P = 0.5), (iii) RCP 4.5 scenario with consumption suppressed by 10% (C P = 0.45) and (iv) RCP 4.5 scenario with consumption enhanced by 10% (C P = 0.55). We assumed fish mass was 6.4 g on Julian day 1 and projected growth through Julian day 365. We estimated mean daily water temperatures for all 7872 reaches using the same two-step approach described above.
We used these simulated growth trajectories to estimate endof-year mass. We also used daily bioenergetics budgets to explore the duration of growing season, which we defined as the number of consecutive days of bioenergetics surplus. Projections of daily bioenergetics budget and end-of-year mass were expressed as the mean of the validated parameter sets. Uncertainty in these projections sets was expressed as the coefficient of variation among validated parameter sets. All BEM were developed and projected using original scripts in R.

Statistical analyses
We used generalized additive models (GAM) to evaluate the spatial (i.e. among reaches) concordance between BEM and SDM projections. We fit three sets of models to test three statistical hypotheses: (i) historical end-of year body size is positively correlated with historical habitat suitability (Hypothesis 1); (ii) historical end-of year body size is positively correlated with change in habitat suitability between historical and future time periods (Hypothesis 2); and (iii) change in end-of-year body size between historical and future time periods is positively correlated with change in habitat suitability between historical and future time periods (Hypothesis 3). Confirmation of Hypothesis 1 would support the biological hypothesis that low habitat suitability limits growth capacity at range margins, whereas high habitat suitability facilitates greater growth capacity within the core of the range. Confirmation of Hypothesis 2 would support the biological hypothesis that historical habitat suitability will maintain conditions suitable for growth capacity in the future. Confirmation of Hypothesis 3 would support the biological hypothesis that historical to future reduction in growth capacity will be caused by the reduction in habitat suitability, whereas increased habitat suitability will facilitate increased growth capacity. Alternatively, no relationship or a negative relationship would indicate that historical spatial variation in habitat suitability or historical to future change in habitat suitability is associated with other aspects of individual performance (e.g. behaviour, reproduction, recruitment, etc.).
For both model sets, SDM-based habitat suitability [i.e. log 2 (PRO+1)] was the predictor variable and BEM-based growth was the response variable. Both model sets also included alternative models with ecoregion ( Supplementary  Fig. S1) fit as a factor because inspection of mapped projections revealed regional differences in BEM and SDM outputs. Models were fit using restricted maximum likelihood in the mgcv library in R (Wood and Scheipl, 2014). Model significance was assessed using an F-test at P < 0.05 and performance was assessed using deviance explained. ables and climate variables (0.79), but poorly when only climate variables were used (0.68). The most important predictor variable was baseflow index (percent change in AUC = −2.01%), followed by January minimum temperature (−1.13%) and soil erodibility factor (−1.09%) (Supplementary Table S1). These relatively small percent changes in AUC suggest that the environmental niche space of GB is not strongly defined by any single environmental gradient.

Species distribution models
SDMs projected the highest historical habitat suitability for streams within the Edwards Plateau, particularly those within the Guadalupe River basin, as well as the mainstem Guadalupe and San Marcos Rivers to the southeast of the Edwards Plateau (Fig. 1B). Projected reach occupancy under historical climate was 2871 reaches, constituting 36.5% of the 7872 perennial reaches within the five-basin range of Guadalupe bass. Occupancy is projected to decrease (−8.7 to −52.1% change) between the historical and future time periods (Table 1). SDM projections that assume only temperature changes (Projections 3 and 6) project more reach extirpations (−29.1% to −42.7% change) than projections that assume only precipitation changes (Projections 4 and 7; −8.7% to −13.1% change), suggesting that historical distributions are limited more by temperature than by precipitation. Extirpations are projected for reaches to the south and east of the Edwards Plateau, whereas relatively few colonizations (Table 1) are projected for reaches to the north of the Edwards Plateau (Fig. 1C, D). This spatial pattern of occupancy transition is similar for projections that assume only temperature changes ( Supplementary Fig. S2A, B). For projections assuming only precipitation changes, extirpations are limited to reaches in the arid southwestern region ( Supplementary  Fig. S2C, D).

Bioenergetics models
The 500 simulated parameter sets projected a wide range of growth trajectories for age-1 Guadalupe bass; however, 56 simulations projected end-of-growing-season simulated length within ±2.5% of end-of-year empirical length in the South Llano River validation reaches ( Fig. 2 Supplementary Fig. S3). We computed the mean (and coefficient of variation) end-of-year mass from these 56 simulations for BEM spatial interpretation. Projected endof-year mass for age-1 fish under historical climate across 2871 historically occupied reaches was median 25.1 g (range among reaches: 16.0 g to 49.3 g). Under future climate, projected end-of-year mass increased to median 45.1 g (range among reaches: 30.0 g to 79.9 g) for the RCP 4.5 scenario and increased to median 61.5 g (range among reaches: 39.8 g to 104.3 g) for the RCP 8.5 scenario. Relative to baseline consumption (C P = 0.50), projected end-of-year mass for the RCP 4.5 scenario decreased to median 19.3 g (range among reaches: 12.1 g to 36.9 g) when consumption was suppressed (C P = 0.45) and increased to median 86.7 g (range among reaches: 59.8 g to 146.9 g) when consumption was enhanced (C P = 0.55). Across the 7872 perennial reaches, end-of-year masses were highest in the south and lowest in the north (Fig. 3A). Coefficients of variation across the 56 different simulations ranged from 5% to 28%, with greater uncertainty (higher CVs) in the north of GB range and along the southern edge of the Edwards Plateau; both areas where projected end-of-year mass was lowest (Fig. 3B). Future changes in end-of-year mass were projected to be proportionally higher for reaches in the north and on the Edwards Plateau (Fig. 3C, D). These reaches with higher future growth were in regions of historically lower temperatures that are projected to warm at a faster rate than regions with historically higher temperatures, owing to the sigmoidal shape of the Punzet et al. (2012) air-water temperature model ( Supplementary Figs S4 and S5). Spatial patterns in end-ofyear growth were similar under suppressed and enhanced consumption relative to baseline consumption ( Supplementary  Fig. S6).
Daily bioenergetic deficits were projected during the cold season and bioenergetic surpluses during the warm season under historical climate (Fig. 4A) jected growing seasons for the RCP 4.5 scenario decreased to 220 days (range 207 days to 259 days) when consumption was suppressed (C P = 0.45) and increased to 264 days (range 247 days to 329 days) when consumption was enhanced (C P = 0.55) (Fig. 4C).
Mass was projected to increase during the growing season under historical climate, the low emissions future climate and the high emissions future climate (Fig. 4D, E). The rate of mass accumulation is more sensitive to changes in consumption relative to changes in climate (Fig. 4E, F). Specifically, the rate of mass accumulation was lower if consumption is suppressed under the low emissions future climate relative to the baseline consumption under historical climate (

Congruence between bioenergetics models and species distribution models
End-of-year mass and habitat suitability during the historical time period were significantly correlated, but the relationship was weak and not monotonic (estimated df = 8.898, reference df = 8.997, F = 87.69, P < 0.01, deviance explained = 9.26%; Fig. 5A). Including ecoregion as a factor increased deviance explained for the model (estimated df = 8.62, reference df = 8.961, F = 34.34, P < 0.01, deviance explained = 68.0%; Fig. 5B), indicating that end-of-year mass varies more by ecoregion than by reach-specific habitat suitability. Thus, Hypothesis 1-historical end-of-year body size is positively correlated with historical habitat suitability-was not supported.
Historical to future change in end-of-year mass and historical habitat suitability were significantly correlated, but the relationship was weak and not monotonic (estimated df = 8.556, reference df = 8.947, F = 210.40, P < 0.01, deviance explained = 19.3%; Fig. 6A). Including ecoregion as a factor increased deviance explained for the model (estimated df = 8.857, reference df = 8.994, F = 349.5, P < 0.01, deviance explained = 28.6%; Fig. 6C). Thus, Hypothesis 2-historical end-of year body size is positively correlated with change in habitat suitability between historical and future time periods-was not supported.
Historical to future change in end-of-year mass and historical to future change in habitat suitability were significantly and positively correlated, indicating concordance between BEM and SDM projections (estimated df = 8.634, reference df = 8.963, F = 28.49, P < 0.01, deviance explained = 47.8%; Fig. 6B). Including ecoregion as a factor increased deviance explained for the model (estimated df = 8.619, reference df = 8.958, F = 300.1, P < 0.01, deviance explained = 59.8%; Fig. 6D). The relationship remained positive for the Edwards Plateau and High Plains (Fig. 6D, green and blue lines), but not for the Coastal Plain (Fig. 6D, red line). Thus, Hypothesis 3-change in end-of-year body size between historical and future time periods is positively correlated with change in habitat suitability between historical and future time periods-was supported.

Historical distribution, ecology and physiology
A fundamental goal of ecology and an objective of this study was to elucidate drivers of geographic range limits.
Geographic range limits are driven by a combination of abiotic, biotic, historical and spatial processes (Holt et al., 2005;Soberón, 2007). These general processes also constrain distributions of freshwater fishes, whereby drainage divides, climate, landscape geology, in-stream habitat and species interactions constrain distribution and abundance of species at broad spatial extents (Jackson et al., 2001;Tonn et al., 1990). Field studies of distribution and habitat use support this paradigm for Guadalupe bass, which associates with spring-fed streams of the Edwards Plateau (Edwards, 1980;Perkin et al., 2010) and ostensibly is geographically restricted by contemporary drainage divides of the western gulf coast (Hoagstrom et al., 2011;Near et al., 2003). Our SDM projections of historical habitat suitability confirmed the spatial association with the Edwards Plateau and the high predictive capability of baseflow index in these models confirmed the association with spring-fed streams (Curtis et al., 2015). Minimum January temperature also was an important predictor of historical distribution, suggesting that cold winter temperatures preclude persistence in the northwestern unoccupied reaches of the Colorado and Brazos River basins. The BEM simulations under historical climate consistently projected energy deficits during the winter months and these deficits are of greater magnitude and persist for a longer duration of the cold season in the north compared to the south. These physiological projections thus support the hypothesis that cold winter temperature could limit the northern range edge of Guadalupe bass and reveals a potential mechanism of winter starvation. BEM-based studies of largemouth bass have shown that cold temperatures and short growing seasons cause starvation of juveniles and drive the cold range edge throughout North America (Fullerton et al., 2000;Garvey et al., 1998). A second (and not necessarily mutually exclusive) interpretation of high predictive capability of minimum January temperature from our SDMs is that warm winter temperatures preclude persistence in the southeastern unoccupied reaches along the gulf coastal plain. SDMs fitted with temperature predictors projected future erosion of habitat suitability along the southeastern range margin relative to models fitted without temperature predictors (Supplementary Fig. 2C, D), supporting the interpretation that winter temperature drives the warm range edge. Whether winter temperature actually constrains the warm-edge limit of Guadalupe bass seems dubious, given that most studies invoke intolerance to low temperature as a constrainer of cold-edge limits among ectotherms generally (Sunday et al., 2011) and fishes specifically (Wood et al., 2020). Nevertheless, one potential mechanism is that a cold winter-and subsequent warming in the spring-is a required seasonal spawning cue for Guadalupe bass as it is for other temperate freshwater fishes (Shuter et al., 2012). Our BEMs revealed explicit insight into growth and implicit insight into recruitment (e.g. size dependent winter survival; sensu Fullerton et al., 2000); however, additional lines of inquiry will be needed to evaluate reproduction, generally, and the seasonal cue hypothesis, specifically (Firkus et al., 2018;Heins, 2020).  A third interpretation is that our SDMs revealed a spurious correlation between winter temperature and other important predictors-a common issue with correlative SDMs when inferring causality (Kearney and Porter, 2009). In Texas, winter and summer temperatures are not strongly correlated (Pearson r = −0.25), meaning that high summer temperature (a poor predictor; Supplementary Table S1) as a driver of warm-edge limits can be ruled out. From a physiological perspective, laboratory growth of Guadalupe bass indicates high optimal temperature for growth (27 • C; Sullivan et al., 2013) relative to historical summer temperatures along the southeastern range edge (<28.5 • C). Thus, it is unlikely that southern range limits are driven by sensitivity of Guadalupe bass to high summer temperatures. The northwest-to-southeast winter temperature gradient spatially correlates with the transition from Edwards Plateau to Coastal Plain and an accompanying suite of geological and biological changes (Hoeinghaus et al., 2007). It is likely that Precipitation did not provide strong predictive capability in our SDMs. This is further supported by the SDM projections under future climate scenarios that revealed similar extirpation and colonization patterns whether precipitation variables were included or not. This finding is surprising given the strong east-to-west precipitation gradient spanning the historical range of Guadalupe bass. Western range limits of Guadalupe bass are almost certainly driven by the eastto-west transition of perennial to intermittent flow regime (Muneepeerakul et al., 2008;Perkin et al., 2015), although our SDMs did not include explicit predictors representing flow regime. While other SDM-based studies have shown that precipitation variables can be useful surrogates of instream flow conditions (McGarvey et al., 2018), this may not apply to all regions, particularly where precipitation gradients span transitions from perennial to intermittent (or ephemeral) streamflow like in Texas. Another potential explanation for poor predictive capacity of precipitation variables is that we included baseflow index-an effective surrogate for perennial flow-in our SDMs and this variable was highly predictive. The interaction between precipitation, baseflow index and the realized streamflow regime is not of the highest importance for characterizing historical range limits, but will be very important when making projections about novel future climate, flow and Guadalupe bass population viability (Birdsong et al., 2015;Jiménez-Valverde et al., 2009).
Competition with a more widespread congener and/or an interaction milieu associated with high-richness paratopic assemblages often limit ranges of endemic species (Graham et al., 2010;Jankowski et al., 2010). We did not explicitly incorporate biotic factors into our SDMs or BEMs, although prey availability and/or interspecific competition may be important drivers of Guadalupe bass range limits, as they are in other freshwater fishes (Finstad et al., 2011;Taniguchi and Nakano, 2000). The eastern range limit of Guadalupe bass corresponds with a rapid increase in fish species richness (Hubbs et al., 2008;Muneepeerakul et al., 2008), which may intensify competition and reduce realized consumption (i.e. reduce the value of the C P parameter in our BEMs). Prey production likely also decreases along southeast-tonorthwest climatic gradients (Patrick et al., 2019), and similarly may reduce realized consumption of Guadalupe bass along the northern and western range limit. Although we did not extrapolate SDMs or BEMs beyond drainage basins in which Guadalupe bass historically have occurred, it is likely that BEMs (and to a lesser extent SDMs) would identify suitable habitat in drainages to the east where thermal regimes are similar (e.g. Trinity and Sabine River basins).
We conclude that historical range limits of Guadalupe bass are driven by multiple factors and that SDMs and BEMs offer different yet informative insights into these drivers. Further refinement of both modelling approachesparticularly the incorporation of (i) proximal abiotic factors (e.g. flow intermittency) and (ii) biotic factors (e.g. spatial and seasonal variation in prey density)-will improve understanding of Guadalupe bass distribution and abundance. Finally, integration with additional modelling approaches that can incorporate spatial variation in reproduction will also improve understanding of range edge dynamics of Guadalupe bass.

Climate change impacts
A primary objective of this study was to assess vulnerability of Guadalupe bass to climate change. Our SDM-based and physiology-based assessments yielded different answers-SDMs forecasted a decline in habitat suitability, whereas BEMs forecasted an increase in growth capacity. Nevertheless, the alternative approaches yielded complementary insights into the spatial distribution of vulnerability and potential mechanisms contributing to vulnerability. Climate envelope approaches (i.e. correlative SDMs; sensu Elith and Leathwick, 2009) often forecast geographic range shifts towards colder and wetter regions (Ackerly et al., 2010;Comte and Grenouillet, 2013). Our SDMs matched this expected response to temperature-habitat suitability erodes at the warm-range edge in the southeastern Coastal Plain. The key environmental variable driving this forecasted erosion-minimum winter temperature-however, is an unlikely driver of warm-range edge retraction (Sunday et al., 2012). Our BEMs, on the other hand, projected an increase in growth potential throughout the historical range, including along the southeastern Coastal Plain. Other physiology-based forecasts indicate similar responses in growth potential for temperate freshwater fishes (Hartman, 2017;Pease and Paukert, 2014) and other temperate ectothermic taxa (Deutsch et al., 2008) under climate change. This increased growth potential is feasible, given that future water temperatures are not forecasted to exceed optimal temperature for growth (27 • C; Sullivan et al., 2013). However, growth potential would have to be matched by increased prey availability and while not being impeded by stronger competition with existing competitors and/or novel competition in reassembled communities (Urban et al., 2012). Indeed, our scenarios of suppressed and enhanced consumption demonstrated that changes in prey quantity or quality are more important than changes in temperature.
On the northern range edge, SDMs projected modest increases habitat suitability that-assuming dispersal opportunities exist-could facilitate colonization of some historically unoccupied reaches in the Colorado and Brazos River basins. BEMs also forecast the highest historical-tofuture increase in growth potential in the northwest (as well as in the southwestern portion of the Edwards Plateau). This is caused by the WT model projecting the greatest increase in WT in regions with 10-20 • C air temperatures (due to sigmoidal air-water relationship; Supplementary Fig. S5; Punzet et al., 2012). This artefact of our simplistic air-water temperature model highlights the importance of improved understanding and forecasting of stream temperatures

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under anticipated changes in air temperature and surfacegroundwater interactions (Caldwell et al., 2020). Surprisingly, our SDMs did not forecast an eastward shift in habitat suitability away from the dry west and towards the wet east. This is likely the result of a breakdown in the causal links between precipitation, in-stream flow conditions and Guadalupe bass occupancy. Indeed, perennial streams on which Guadalupe bass rely are forecasted to decline across historical Guadalupe bass range, particularly in the arid western portions of the Colorado and Brazos River basins (Smith et al., 2013). Thus, it is likely that Guadalupe bass populations on the western range edge will be lost as their habitats transition from perennial to intermittent . Given the contribution of the Edwards-Trinity Aquifer to surface flow of perennial streams, the extent and magnitude of dewatering will depend on amplifying interactions between climate change (i.e. as we have projected) and groundwater extraction (i.e. not explicitly included in our SDMs). We did not project habitat suitability or growth potential in the basins to the east of the Brazos River basin (i.e. Trinity and Sabine River basins) because there are no natural dispersal opportunities. Whether habitat suitability and/or growth potential would increase in these adjacent basins under future climate would likely depend on in-stream conditions (e.g. proximal correlates of baseflow index).
Another objective of our BEM assessment was to evaluate seasonal changes in Guadalupe bass growth caused by warming in the 21st century. Our BEMs indicate an energy deficit during the cold season when metabolic costs exceed consumptive gains. Warmer year-round temperatures throughout Guadalupe bass range under future climate therefore are projected to advance the onset of feeding in the spring and extend feeding in the autumn, yielding a longer growing season and larger end-of-year body size. Under historical temperature regimes, Guadalupe bass are projected to maintain positive growth even in the hottest period of summer because consumptive gains can exceed metabolic losses. For future projections, particularly the RCP 8.5 scenario, metabolic costs are projected to approach or even exceed consumptive gains for several of the parameter sets meaning that growth could be suppressed both in winter and mid-summer. These changes in seasonal energy budgets will likely affect allocation of energy to somatic growth versus reproduction and subsequent population dynamics.

Broader implications
Mechanistic niche modelling was formalized by Kearney and Porter (2009) and has informed basic research aimed at understanding the ecological processes driving species distributions as well as applied research aimed at forecasting species vulnerability to anthropogenic environmental change (e.g. Briscoe et al., 2016). Nevertheless, implementation has focused largely on terrestrial insects (e.g. Buckley et al., 2011), amphibians (e.g. Kearney et al., 2008) and reptiles (e.g. Buckley, 2008). The BEM approach used by fisheries professionals, partic-ularly the Wisconsin model, conceptually aligns with mechanistic niche modelling (sensu Kearney and Porter, 2009), but to date is not implemented at fine spatial resolutions (i.e. individual reaches) while spanning broad spatial extents (i.e. 1000s of reaches). Our study demonstrates that this novel application of BEM is generalizable to any region or fish species. Nevertheless, several challenges currently preclude broader implementation of BEMs as tools to forecast climate change vulnerability of fishes in a spatially explicit manner.
First, a challenge of developing physiology-based conservation assessments is that physiological traits of species in need of conservation are often poorly known. For example, 39% of the 700-plus species of North American freshwater fishes are imperilled (Jelks et al., 2008), yet the Wisconsin model has been parameterized for only 73 fish species worldwide (Deslauriers et al., 2017), few of which are among the 270plus imperilled species in North America. In the near term, the parameter resampling approach we used (sensu Petersen et al., 2008)-combined with validation from field growth studies-represents an effective strategy for rapid development of BEMs for numerous imperilled species. Indeed, data on field growth are generally available and have recently been compiled and synthesized for broad-scale meta-analyses (e.g. Rypel, 2014). In the long term, physiological reaction norms for functionally and phylogenetically underrepresented taxa should be accumulated (Chown and Gaston, 2016). Second, high spatiotemporal resolution temperature data for freshwater habitats are needed. Indeed, poorly known airwater temperature relationships and the influence of surfacegroundwater interactions contributed to uncertainty in our BEM projections. Current open-source datasets tend to be spatially comprehensive, often spanning tens of thousands of reaches, but lack daily temporal resolution needed for BEM (NorWeST; Isaak et al., 2017). It is likely that spatiotemporally comprehensive data will become available in the near future (e.g. Hydroclim; hydroclim.org) as the opensource data culture grows (McManamay and Utz, 2014). Third, spatiotemporally comprehensive biological information is needed to better understand how predator-prey interactions (i.e. spatiotemporal variation in C P parameter) and behaviour (i.e. spatiotemporal variation in ACT parameter) vary along environmental gradients and among seasons. Surrogates of these factors can be acquired from existing geospatial datasets (e.g. net primary production; Deines et al., 2015) or statistical models (e.g. secondary production; Patrick et al., 2019). This information will facilitate the adjustment of C P and ACT parameters in BEMs to more realistically project energy budgets of fish.

Conclusions
Our work demonstrates the utility of applying an ensemble of approaches to assess ecological responses to future climate change (Hollowed et al., 2013)

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suitability based on SDM and growth capacity based on BEM indicated no spatial concordance between the two approaches, whereas comparison between historical to future change in these responses revealed spatial concordance between the two approaches. The emergent pattern is that habitat suitability may or may not correlate with potential for end-of-year mass, perhaps because increases in temperature are interpreted differently between modelling approaches. In the BEM approach, warmer temperatures that do not exceed critical thermal maxima result in greater gain in mass (assuming unlimited resources for growth); whereas, in the SDM approach, correlative temperature relationships equate to occurrence based on unknown and unmeasured mechanisms. It is possible that unmet increases in energetic demand under greater temperatures is a mechanism for the modelled reduction in habitat suitability as discovered in other systems (Johansen et al., 2015;Morgan et al., 2001), particularly in the High Plains and Edwards Plateau regions where warming is projected to increase most. Additional laboratory research could test this hypothesis. Regardless, the message remains that inference into the effects of climate change on fish distributions depends on the methodology used in the assessment and our results point to the potential for unique and perhaps synergistic insights if BEM are integrated to a greater extent.

FUNDING
There is no funding to report.

DATA AVAILABILITY
Data inputs were derived from sources in the public domain. Novel data were generated using original scripts and are available on the GitHub repository: https://github.com/troiamj/ GB_BEM_v01.

Supplementary material
Supplementary material is available at Conservation Physiology online.